Generating time-ordered globally unique revision numbers

ABSTRACT

A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN). The method begins by receiving a data object for storage in a plurality of storage vaults. The method continues by encoding the data object in accordance with dispersal parameters of the storage vault to produce a corresponding plurality of sets of encoded data slices. The method continues by generating a unique revision number to associate with the data object. The method continues by facilitating storage of the corresponding plurality of sets of encoded data slices with the unique revision number and facilitating data synchronization between the plurality of storage vaults based on the unique revision numbers of stored data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120, as a continuation-in-part (CIP) of U.S. Utility patentapplication Ser. No. 15/661,332, entitled “SYNCHRONOUSLY STORING DATA INA PLURALITY OF DISPERSED STORAGE NETWORKS,” filed Jul. 27, 2017, whichclaims priority as a continuation-in-part (CIP) of U.S. Utility patentapplication Ser. No. 14/927,446, entitled “SYNCHRONIZING STORAGE OF DATACOPIES IN A DISPERSED STORAGE NETWORK,” filed Oct. 29, 2015, now U.S.Pat. No. 9,727,427, issued on Aug. 8, 2017, which claims prioritypursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No.62/098,449, entitled “SYNCHRONOUSLY STORING DATA IN A PLURALITY OFDISPERSED STORAGE NETWORKS,” filed Dec. 31, 2014, all of which arehereby incorporated herein by reference in their entirety and made partof the present U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of another dispersed storage networkin accordance with the present invention; and

FIG. 9A is a flowchart illustrating an example of synchronously storingsimilar data in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-9A. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generateper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the 10 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

In one embodiment, when detecting objects that are not synchronized, itis useful to leverage time-ordered revision numbers which are unlikelyto collide. If collisions occur and two writers select the same revisionnumber (by chance) when updating two different versions of the sameobject, then this desynchronized state may go unnoticed bysynchronization agents (who consider them to be synchronized for theirsources contain the same revision). To overcome this, revision numberscan be generated using the following approach: 1. Partition the bits ofthe revision number into three different areas: a time portion, anoperation count portion, and a random portion. 2. Use the mostsignificant bits of the revision number to reflect the time portion. 3.Choose a number of bits for the operation count portion (L) to be suchthat no more than 2^(L) operations would be generated for the sameobject by the same writer for the time resolution of the time portion.For example, if the time portion is relative to the millisecondprecision, and it is impossible for writers to perform more than 128update operations on the same object within 1 millisecond, then 7 bitswould be adequate for the operation count portion of the revision. 4.Use the remaining (least significant) bits of the revision field tocontain a random, pseudo-random, or deterministic function applied onthe content of the data to fill in this field. The purpose of thisportion is to decrease the likelihood of collisions when distinct andisolated DS-processing units are independently updating the same objecton two or more vaults that are part of the same synchronization set. Thetime and update numbers ensure that the revision number is alwaysincreasing, while the random component ensures a low probability thatindependent writers would conflict when updating the same object in adifferent way, even if it happens to occur at the same time.

To handle clock desynchronize issues, a DS processing unit whose localtime is less than the time indicated in a revision being updated mayinstead simply increment that time field by one, or update its localclock to equal that time, such that no DS processing unit writes arevision “backwards in time” from the perspective of other DS units.

FIG. 9 is a schematic block diagram of another dispersed storage network(DSN) that includes a plurality of storage vaults, the network 24 ofFIG. 1, and at least two distributed storage and task (DST) processingunits 1-2. Each DST processing unit may be implemented utilizing the DSTprocessing unit 16 (computing device) of FIG. 1. The plurality ofstorage vaults may be implemented utilizing one or more sets of DSTexecution (EX) units. Each set of DST execution units may include anynumber of DST execution units. For example, vault 1 is implemented toinclude a first set of DST execution units 1-1 through 1-n, vault 2 isimplemented to include a second set of DST execution units 2-1 through2-n, etc. through vault V that is implemented to include a “Vth” set ofDST execution units V-1 through V-n. Each DST execution unit may beimplemented utilizing the storage unit 36 of FIG. 1.

The DSN functions to synchronously store similar data in the pluralityof storage vaults. In an example of operation of the synchronous storageof the similar data, the two or more DST processing units receive a dataobject for storage. For example, DST processing units 1-2 receives adata object A for storage in the plurality of storage vaults.Alternatively, the two or more DST processing units substantiallysimultaneously receive a unique data object, where each unique dataobject is associated with a common data identifier.

Having received the data object for storage, each DST processing unit,for each storage vault, dispersed storage error encodes the data objectin accordance with dispersal parameters of the storage vault to producea corresponding plurality of sets of encoded data slices. For example,DST processing units 1-2 each obtains the dispersal parameters for thestorage vault, and when the dispersal parameters are unique, dispersedstorage error encodes the data object A to produce the plurality of setsof encoded data slices. Alternatively, when the dispersal parameters arenot unique, each DST processing unit reuses another plurality of sets ofencoded data slices.

Having generated the encoded data slices, each DST processing unitgenerates a unique revision number (time-ordered globally uniquerevision number) to be associated with all of the plurality of sets ofencoded data slices. The DST processing unit generates the uniquerevision number to include a time-based portion, an operation countportion, and a watermark portion. The time-based portion includes areal-time indicator that is ever increasing. For example, the DSTprocessing unit interprets a system clock to produce the time-basedportion. The operation count portion includes an ever-increasing numberfor a series of related operations. For example, the DST processing unit16 chooses a number of bits L for the operation count portion such thatno more than 2^(L) operations can be generated for the same data objectby the same DST processing unit for a time resolution of the time-basedportion. For instance, the DST processing unit is limited to 128 updateoperations on the same data object within one millisecond when the timeresolution is one millisecond and the number of bits of the operationcount portion is 7 (e.g., L=7). The watermark portion includes at leastone of a random number, a pseudorandom number, or a result of applyingthe deterministic function to at least one of the data object, the dataidentifier, a vault identifier (ID), or a requesting entity ID.

Having generated the unique revision number, for each storage vault,each DST processing unit facilitates storage of the correspondingplurality of sets of encoded data slices utilizing the correspondingunique revision number. For example, DST processing unit 1 issues, viathe network 24, a set of write slice requests to the DST execution unitsof the storage vault, where the set of write slice requests includes thecorresponding plurality of sets of encoded data slices and thecorresponding unique revision number.

Subsequent to the storage of the plurality of sets of encoded dataslices, at least one of the DST processing units and a synchronizingagent facilitates a data synchronization process based on the uniquerevision numbers of the store data such that a plurality of sets ofencoded data slices of the corresponding data object is stored in eachstorage vault for each unique revision number.

FIG. 9A is a flowchart illustrating an example of synchronously storingsimilar data. The method includes step 470 where a processing unit oftwo or more processing units receives a data object for storage in aplurality of storage vaults. For example, each processing unit receivesa common data object. As another example, each processing unit receivesa different data object that share a common data identifier. Thereceiving includes at least one of receiving the data objectsubstantially simultaneously and a first processing unit generating thedata object and a second processing unit receiving the data object.

For each storage vault, the method continues at step 472 where eachprocessing unit encodes the data object in accordance with dispersalparameters of the storage vault to produce a corresponding plurality ofsets of encoded data slices. For example, each processing unit obtainsthe dispersal parameters and dispersed storage error encodes the dataobject to produce the corresponding plurality of sets of encoded dataslices

The method continues at step 474 where each processing unit generates aunique revision number to associate with the data object. For example,each processing unit generates the unique revision number to include atleast one ever increasing portion and at least one watermark portion.The ever-increasing portion includes one or more of an ever-increasingtime portion or an ever-increasing operation portion. The watermarkportion includes at least one of a random number, a pseudorandom number,or a result of applying a deterministic function to at least a portionof one or more of the data object or the data object identifier.

For each storage vault, the method continues at step 476 where eachprocessing unit facilitates storage of the corresponding plurality ofsets of encoded data slices with the unique revision number. Forexample, each processing unit issues one or more sets of write slicerequests to the storage vault, where the write slice requests includethis corresponding plurality of sets of encoded data slices and thecorresponding unique revision number.

The method continues at step 478 where at least one processing unitfacilitates data synchronization between the plurality of storage vaultsbased on the unique revision numbers of stored data. For example, theprocessing unit maintains both or eliminates a revision. Whenmaintaining, the processing unit maintains both revisions of a commondata object in accordance with a predetermination when theever-increasing portion is substantially the same. As another example,the processing unit selects a revision for elimination by at least oneof a random selection, selecting a revision with a highestever-increasing portion, and selecting a revision with a watermarkportion corresponding to a priority revision.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other computing devices. In addition, at least one memorysection (e.g., a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices of the dispersedstorage network (DSN), cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by processing units of oneor more computing devices of a dispersed storage network (DSN), themethod comprises: receiving, by two or more of the processing units, adata object for storage in a plurality of storage vaults, wherein theplurality of storage vaults includes two or more storage vaults for thedata object; for each storage vault of the plurality of storage vaults,encoding the data object in accordance with dispersal parameters of thestorage vault to produce a corresponding plurality of sets of encodeddata slices; generating, by each processing unit of the two or more ofthe processing units, a unique revision number to associate with thedata object, wherein the unique revision number is a time-orderedglobally unique revision number; for each storage vault, each processingunit of the two or more of the processing units facilitating storage ofthe corresponding plurality of sets of encoded data slices with theunique revision number; and facilitating, by at least one of the two ormore of the processing units, data synchronization between the pluralityof storage vaults based on the unique revision number of a stored dataobject.
 2. The method of claim 1, wherein the receiving a data objectfor storage includes receiving a common data object.
 3. The method ofclaim 1, wherein the receiving a data object for storage includes adifferent data object that share a common data identifier.
 4. The methodof claim 1, wherein the receiving a data object for storage includes atleast one of receiving the data object substantially simultaneously anda first of the processing units generating the data object and a secondof the processing units receiving the data object.
 5. The method ofclaim 1, wherein the encoding the data object includes obtaining thedispersal parameters and dispersed storage error encoding the dataobject to produce the corresponding plurality of sets of encoded dataslices.
 6. The method of claim 1, wherein the generating the uniquerevision number includes at least one ever-increasing portion and atleast one watermark portion.
 7. The method of claim 6, wherein the atleast one ever-increasing portion includes one or more of anever-increasing time portion or an ever-increasing operation portion. 8.The method of claim 6, wherein the watermark portion includes at leastone of a random number, a pseudorandom number, or a result of applying adeterministic function to at least a portion of one or more of the dataobject or a data object identifier.
 9. The method of claim 6, whereinthe maintaining includes maintaining both revisions of a common dataobject in accordance with a predetermination when the ever-increasingportion is substantially the same.
 10. The method of claim 1, whereinthe facilitating storage includes issuing one or more sets of writeslice requests to the storage vault, wherein the write slice requestsinclude this corresponding plurality of sets of encoded data slices anda corresponding unique revision number.
 11. The method of claim 1,wherein the facilitating data synchronization includes maintaining bothunique revision numbers or eliminating unique revision numbers.
 12. Themethod of claim 11, wherein the maintaining includes selecting arevision for elimination by at least one of a random selection,selecting a revision with a highest ever-increasing portion, andselecting a revision with a watermark portion corresponding to apriority revision.
 13. A computing device of a group of computingdevices of a dispersed storage network (DSN), the computing devicecomprises: an interface; a local memory; and a processing moduleoperably coupled to the interface and the local memory, wherein theprocessing module functions to: receive a data object for storage in aplurality of storage vaults, wherein the plurality of storage vaultsincludes two or more storage vaults for the data object; encode the dataobject in accordance with dispersal parameters of the storage vault toproduce a corresponding plurality of sets of encoded data slices;generate a unique revision number to associate with the data object,wherein the unique revision number includes at least one ever-increasingportion and at least one watermark portion; facilitate storage of thecorresponding plurality of sets of encoded data slices with the uniquerevision number; and facilitate data synchronization between theplurality of storage vaults based on the unique revision number of astored data object.
 14. The computing device of claim 13, wherein the atleast one ever-increasing portion includes one or more of anever-increasing time portion or an ever-increasing operation portion.15. The computing device of claim 13, wherein the watermark portionincludes at least one of a random number, a pseudorandom number, or aresult of applying a deterministic function to at least a portion of oneor more of the data object or a data object identifier.
 16. Thecomputing device of claim 13, wherein the facilitate storage includesissuing one or more sets of write slice requests to the storage vault,wherein the write slice requests include this corresponding plurality ofsets of encoded data slices and a corresponding unique revision number.17. A method for execution by processing modules of one or morecomputing devices of a dispersed storage network (DSN), the methodcomprises: receiving a data object for storage in a plurality of storagevaults, wherein the plurality of storage vaults includes two or morestorage vaults for the data object; for each storage vault of theplurality of storage vaults, encoding the data object in accordance withdispersal parameters of the storage vault to produce a correspondingplurality of sets of encoded data slices; generating a unique revisionnumber to associate with the data object, wherein the unique revisionnumber includes at least one ever-increasing portion and at least onewatermark portion; for each storage vault, facilitating storage of thecorresponding plurality of sets of encoded data slices with the uniquerevision number; and facilitating data synchronization between theplurality of storage vaults based on the unique revision number of astored data object.
 18. The method of claim 17, wherein the at least oneever-increasing portion includes one or more of an ever-increasing timeportion or an ever-increasing operation portion.
 19. The method of claim17, wherein the watermark portion includes at least one of a randomnumber, a pseudorandom number, or a result of applying a deterministicfunction to at least a portion of one or more of the data object or adata object identifier.
 20. The method of claim 17, wherein thefacilitating data synchronization includes maintaining both revisions ofa common data object in accordance with a predetermination when theever-increasing portion is substantially the same.